Predictor object
A Predictor object holds any machine learning model (mlr, caret, randomForest, ...) and the data to be used for analyzing the model. The interpretation methods in the iml package need the machine learning model to be wrapped in a Predictor object.
A Predictor object is a container for the prediction model and the data. This ensures that the machine learning model can be analyzed in a robust way.
Note: In case of classification, the model should return one column per class with the class probability.
library("mlr") task <- makeClassifTask(data = iris, target = "Species") learner <- makeLearner("classif.rpart", minsplit = 7, predict.type = "prob") mod.mlr <- train(learner, task) mod <- Predictor$new(mod.mlr, data = iris) mod$predict(iris[1:5, ]) mod <- Predictor$new(mod.mlr, data = iris, class = "setosa") mod$predict(iris[1:5, ]) library("randomForest") rf <- randomForest(Species ~ ., data = iris, ntree = 20) mod <- Predictor$new(rf, data = iris, type = "prob") mod$predict(iris[50:55, ]) # Feature importance needs the target vector, which needs to be supplied: mod <- Predictor$new(rf, data = iris, y = "Species", type = "prob")
data: data.frame
Data object with the data for the model interpretation.
model: (any)
The machine learning model.
batch.size: numeric(1)
The number of rows to be input the model for prediction at once.
class: character(1)
The class column to be returned.
prediction.colnames: character
The column names of the predictions.
prediction.function: function
The function to predict newdata.
task: character(1)
The inferred prediction task: `"classification"` or `"regression"`.
new()Create a Predictor object
Predictor$new(
model = NULL,
data = NULL,
predict.function = NULL,
y = NULL,
class = NULL,
type = NULL,
batch.size = 1000
)
model: any
The machine learning model. Recommended are models from `mlr` and `caret`. Other machine learning with a S3 predict functions work as well, but less robust (e.g. `randomForest`).
data: data.frame
The data to be used for analyzing the prediction model. Allowed column classes are: numeric , factor , integer , ordered and character
For some models the data can be extracted automatically. `Predictor$new()` throws an error when it can't extract the data automatically.
predict.function: function
The function to predict newdata. Only needed if `model` is not a model from `mlr` or `caret` package. The first argument of `predict.fun` has to be the model, the second the `newdata`:
```
function(model, newdata)
```
y: character(1) | numeric | factor
The target vector or (preferably) the name of the target column in the `data` argument. Predictor tries to infer the target automatically from the model.
class: character(1)
The class column to be returned. You should use the column name of the predicted class, e.g. `class="setosa"`.
type: character(1))
This argument is passed to the prediction function of the model. For regression models you usually don't have to provide the type argument. The classic use case is to say `type="prob"`
for classification models. Consult the documentation of the machine learning package you use to find which type options you have. If both `predict.fun` and `type` are used, then type is passed as an argument to `predict.fun`.
batch.size: numeric(1)
The maximum number of rows to be input the model for prediction at once. Currently only respected for FeatureImp , Partial and Interaction .
predict()Predict new data with the machine learning model.
Predictor$predict(newdata)
newdata: data.frame
Data to predict on.
print()Print the Predictor object.
Predictor$print()
clone()The objects of this class are cloneable with this method.
Predictor$clone(deep = FALSE)
deep: Whether to make a deep clone.
Useful links